The achievable performance of convex demixing
Information Theory
2013-10-01 v1 math.IT
Optimization and Control
Abstract
Demixing is the problem of identifying multiple structured signals from a superimposed, undersampled, and noisy observation. This work analyzes a general framework, based on convex optimization, for solving demixing problems. When the constituent signals follow a generic incoherence model, this analysis leads to precise recovery guarantees. These results admit an attractive interpretation: each signal possesses an intrinsic degrees-of-freedom parameter, and demixing can succeed if and only if the dimension of the observation exceeds the total degrees of freedom present in the observation.
Keywords
Cite
@article{arxiv.1309.7478,
title = {The achievable performance of convex demixing},
author = {Michael B. McCoy and Joel A. Tropp},
journal= {arXiv preprint arXiv:1309.7478},
year = {2013}
}